---
title: "babette demo"
author: "Richèl J.C. Bilderbeek"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{babette demo}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r check_empty_cache_at_start, include = FALSE}
# This is only needed to pass the CRAN Windows build.
#
# This vignette is tested to clean up nicely on GitHub Actions
# and r-hub on Windows
#
beastier::remove_beaustier_folders()
beastier::check_empty_beaustier_folders()
```


## Introduction

![](babette_logo.png)

This vignette briefly demonstrates multiple features of `babette`,
without going into much detail.

First, load the library:

```{r load_babette, results='hide', warning=FALSE, error=FALSE, message=FALSE}
library(babette)
```

This vignette shows how to:

 * Let `babette` run 'BEAST2'
 * Plot the posterior estimates
 * Show the effective sample sizes (ESS)
 * Show the summary statistics
 * Plot the posterior phylogenies

In all cases, this is done for a short MCMC chain length of 10K:

```{r}
inference_model <- create_test_inference_model()
```
Also, in all cases, we use the same BEAST2 options:

```{r}
beast2_options <- create_beast2_options(verbose = TRUE)
```


## Let `babette` run 'BEAST2'

For an alignment, we'll use a `babette` example alignment.

```{r}
if (is_beast2_installed()) {
  out <- bbt_run_from_model(
    fasta_filename = get_babette_path("anthus_aco_sub.fas"),
    inference_model = inference_model,
    beast2_options = beast2_options
  )
  bbt_delete_temp_files(
    inference_model = inference_model,
    beast2_options = beast2_options
  )
}
```

## Plot the posterior estimates

```{r}
if (is_beast2_installed()) {
  library(ggplot2)
  p <- ggplot(
    data = out$estimates,
    aes(x = Sample)
  )
  p + geom_line(aes(y = TreeHeight), color = "green")
  p + geom_line(aes(y = YuleModel), color = "red")
  p + geom_line(aes(y = birthRate), color = "blue")
}
```

## Show the effective sample sizes (ESS)

Effective sample sizes, with 20% burn-in removed:

```{r}
if (is_beast2_installed()) {
  traces <- remove_burn_ins(
    traces = out$estimates,
    burn_in_fraction = 0.2
  )
  esses <- t(
    calc_esses(
      traces,
      sample_interval = inference_model$mcmc$tracelog$log_every
    )
  )
  colnames(esses) <- "ESS"
  knitr::kable(esses)
}
```

For a reliable inference, use an ESS of at least 200.

## Show the summary statistics

```{r}
if (is_beast2_installed()) {
  sum_stats <- t(
    calc_summary_stats(
      traces$posterior,
      sample_interval = inference_model$mcmc$tracelog$log_every
    )
  )
  colnames(sum_stats) <- "Statistic"
  knitr::kable(sum_stats)
}
```

## Plot the posterior phylogenies

```{r fig.width=7, fig.height=7}
if (is_beast2_installed()) {
  plot_densitree(out$anthus_aco_sub_trees, width = 2)
}
```

```{r check_empty_cache_at_end, include = FALSE}
# This is only needed to pass the CRAN Windows build.
#
# This vignette is tested to clean up nicely on GitHub Actions
# and r-hub on Windows
#
beastier::remove_beaustier_folders()
beastier::check_empty_beaustier_folders()
```